Systems and methods for identifying illegitimate activities based on graph-based distance metrics

ABSTRACT

Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/582,337, filed on Apr. 28, 2017 and entitled “SYSTEMS AND METHODS FORIDENTIFYING ILLEGITIMATE ACTIVITIES BASED ON GRAPH-BASED DISTANCEMETRICS”, which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present technology relates to the field of identifying illegitimateactivities. More particularly, the present technology relates to systemsand methods for identifying illegitimate activities in networkedenvironments based on graph-based distance metrics.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices, for example,to interact with one another, create content, share content, and viewcontent. In some cases, a user can utilize his or her computing deviceto access a social networking system (or service). The user can provide,post, share, and access various content items, such as status updates,images, videos, articles, and links, via the social networking system.

Various types of content can be created and presented in the socialnetworking system. In some cases, content in the social networkingsystem can be fraudulent or illegitimate. For example, certainadvertisements can promote fake products or scams. When a user accountis involved in a transaction or activity that is identified asfraudulent or illegitimate, the user account can be labeled as anillegitimate account.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured togenerate a node graph comprising a plurality of user account nodes and aplurality of edge nodes connecting the plurality of user account nodes.A distance score is calculated for each user account node of theplurality of user account nodes. It is determined that a transaction isan illegitimate transaction based on the distance scores.

In an embodiment, a subset of the plurality of user account nodes areillegitimate user account nodes.

In an embodiment, the distance score for a user account node representsa minimum distance from the user account node to a nearest illegitimateuser account node.

In an embodiment, the distance score is calculated based on a modifiedbreadth-first search algorithm.

In an embodiment, the plurality of edge nodes are associated with anedge characteristic.

In an embodiment, the edge characteristic comprises at least one of: IPaddress, advertisement landing page ID, computing device identificationinformation, or payment information.

In an embodiment, each edge node of the plurality of edge nodes isassociated with a particular IP address, a particular advertisementlanding page ID, a particular set of computing device identificationinformation, or a particular credit card BIN.

In an embodiment, a plurality of node graphs are generated. Each nodegraph of the plurality of node graphs comprises a plurality of useraccount nodes and a plurality of edge nodes. Each node graph of theplurality of node graphs is associated with a particular edgecharacteristic. A plurality of distance scores are calculated for eachuser account node in the plurality of user account nodes. Each distancescore of the plurality of distance scores is calculated based on arespective one of the plurality of node graphs.

In an embodiment, the transaction is denied based on the determiningthat the transaction is an illegitimate transaction.

In an embodiment, a subset of the plurality of edge nodes areillegitimate edge nodes, and the distance score for a user account noderepresents a minimum distance from the user account node to a nearestillegitimate edge node.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including a graph-based distancemetrics module, according to an embodiment of the present disclosure.

FIG. 2 illustrates a functional block diagram associated withidentification of illegitimate activity based on graph-based distancemetrics, according to various embodiments of the present disclosure.

FIGS. 3A-3B illustrate example bipartite node graphs from whichgraph-based distance metrics can be determined, according to variousembodiments of the present disclosure.

FIG. 4 illustrates an example method associated with identification ofillegitimate activity based on graph-based distance metrics, accordingto an embodiment of the present disclosure.

FIG. 5 illustrates an example method associated with identification ofillegitimate activity based on graph-based distance metrics from aplurality of node graphs, according to an embodiment of the presentdisclosure.

FIG. 6 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 7 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Identifying Illegitimate Activities

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices, for example,to interact with one another, create content, share content, and viewcontent. In some cases, a user can utilize his or her computing deviceto access a social networking system (or service). The user can provide,post, share, and access various content items, such as status updates,images, videos, articles, and links, via the social networking system.

Various types of content can be created and presented in the socialnetworking system. In some cases, content in the social networkingsystem can be fraudulent or illegitimate. For example, certainadvertisements can promote fake products or scams. When a user accountis involved in a transaction or activity that is identified asfraudulent or illegitimate, such as posting fraudulent content, the useraccount can be labeled as an illegitimate account. Conventionalapproaches can detect fraudulent activity based on historical data aboutuser accounts and various transactions or activities undertaken by thoseuser accounts. While historical data pertaining to a particular useraccount can be useful in identifying additional fraudulent orillegitimate activity by that particular user account, it remains achallenge to identify illegitimate activity by user accounts that do nothave a history of illegitimate activity and have not yet been labeled asillegitimate accounts.

An improved approach rooted in computer technology overcomes theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology. Ingeneral, one or more node graphs can be generated which relate useraccounts (e.g., social networking system user accounts) to one another.For example, a node graph can relate user accounts to one another basedon a particular characteristic, such as IP address. The node graph caninclude a plurality of user account nodes, wherein each user accountnode is associated with a particular user account. The node graph caninclude edges between user account nodes. An edge connecting two useraccount nodes can indicate a shared characteristic between the two useraccount nodes and, therefore, their associated user accounts. In certainembodiments, the node graph can be a bipartite node graph, whichincludes both user account nodes and edge nodes. In such embodiments,“edges” can be represented as edge nodes. In such embodiments, ratherthan an edge connecting a first user account node to a second useraccount node, the first user account node can be connected to a firstedge node and the first edge node can also be connected to the seconduser account node. The shared edge node between the first and seconduser account nodes can indicate a shared characteristic between thefirst and second user account nodes.

Certain user account nodes in the node graph can be labeled asillegitimate user account nodes, wherein each illegitimate user accountnode is associated with an illegitimate user account. Once a node graphis generated, a distance score can be calculated for each user accountnode in the node graph. The distance score for a user account node canbe equal to a minimum distance between the user account node and thenearest illegitimate user account node. When a distance score iscalculated for a user account node, the distance score can be associatedwith a user account associated with the user account node.

In various embodiments, multiple node graphs can be generated, withedges and/or edge nodes in each node graph being associated with aparticular characteristic. For example, a first node graph can begenerated in which edge nodes in the first node graph are associatedwith a first characteristic; a second node graph can be generated inwhich edge nodes in the second node graph are associated with a secondcharacteristic; and so forth. Various characteristics that can beassociated with edges and/or edge nodes can include, for example, IPaddress, computing device identification information, advertisementlanding page ID, payment information (e.g., credit card number, creditcard BIN), and the like. Multiple distance scores can be calculated fora user account based on the multiple node graphs. Distance scores forone or more user accounts can be used to identify illegitimateactivities. For example, an illegitimate activity identification modelcan be configured to identify illegitimate activities based, at least inpart, on distance scores. More details regarding the present disclosureare provided herein.

FIG. 1 illustrates an example system 100 including an examplegraph-based distance metrics module 102, according to an embodiment ofthe present disclosure. The graph-based distance metrics module 102 canbe configured to generate one or more node graphs which relate useraccounts to one another. A node graph can include a plurality of useraccount nodes, wherein each user account node is associated with aparticular user account. The node graph can include edges between useraccount nodes. An edge connecting two user account nodes can indicate ashared characteristic between the two user account nodes and, therefore,the user accounts associated with the two user account nodes. Forexample, if the graph-based distance metrics module 102 generates a nodegraph that relates user accounts to one another based on IP address, anedge between a first user account node and a second user account nodecan indicate a shared IP address between a first user account associatedwith the first user account node and a second user account associatedwith the second user account node.

In certain embodiments, a node graph can be a bipartite node graph,which includes both user account nodes and edge nodes. In suchembodiments, “edges” connecting two user account nodes are representedas edge nodes placed between the two user account nodes. In suchembodiments, rather than an edge connecting a first user account node toa second user account node, the first user account node can be connectedto a first edge node and the first edge node can also be connected tothe second user account node. The shared edge node between the first andsecond user account nodes can indicate a shared characteristic betweenthe first and second user account nodes.

The graph-based distance metrics module 102 can be configured to labelcertain user account nodes in a node graph as illegitimate user accountnodes. Each illegitimate user account node is associated with anillegitimate user account. The graph-based distance metrics module 102can be further configured to calculate a distance score for each useraccount node in a node graph. The distance score for a user account nodecan be equal to a minimum distance between the user account node and thenearest illegitimate user account node. The graph-based distance metricsmodule 102 can associate a distance score calculated for a user accountnode with a user account associated with the user account node.

In various embodiments, the graph-based distance metrics module 102 canbe configured to generate multiple node graphs, with edges and/or edgenodes in each node graph being associated with a particularcharacteristic. For example, the graph-based distance metrics module 102can generate a first node graph in which edge nodes in the first nodegraph are associated with a first characteristic; the graph-baseddistance metrics module 102 can generate a second node graph in whichedge nodes in the second node graph are associated with a secondcharacteristic; and so forth. Various characteristics that can beassociated with edges and/or edge nodes can include, for example, IPaddress, computing device identification information, advertisementlanding page ID, payment information (e.g., credit card number or creditcard BIN), and the like. The graph-based distance metrics module 102 cancalculate multiple distance scores for each user account/user accountnode based on the multiple node graphs. The graph-based distance metricsmodule 102 can be configured to identify illegitimate activities basedon the distance scores.

As shown in the example of FIG. 1, the graph-based distance metricsmodule 102 can include a graph generation module 104, a distance scorecalculation module 106, and an illegitimate activity identificationmodule 108. In some instances, the example system 100 can include atleast one data store 110. The components (e.g., modules, elements, etc.)shown in this figure and all figures herein are exemplary only, andother implementations may include additional, fewer, integrated, ordifferent components. Some components may not be shown so as not toobscure relevant details. In various embodiments, one or more of thefunctionalities described in connection with the graph-based distancemetrics module 102 can be implemented in any suitable combinations.

In some embodiments, the graph-based distance metrics module 102 can beimplemented, in part or in whole, as software, hardware, or anycombination thereof. In general, a module, as discussed herein, can beassociated with software, hardware, or any combination thereof. In someimplementations, one or more functions, tasks, and/or operations ofmodules can be carried out or performed by software routines, softwareprocesses, hardware, and/or any combination thereof. In some cases, thegraph-based distance metrics module 102 can be implemented, in part orin whole, as software running on one or more computing devices orsystems, such as on a user or client computing device. For example, thegraph-based distance metrics module 102, or at least a portion thereof,can be implemented as or within an application (e.g., app), a program,or an applet, etc., running on a user computing device or a clientcomputing system, such as the user device 610 of FIG. 6. In anotherexample, the graph-based distance metrics module 102, or at least aportion thereof, can be implemented using one or more computing devicesor systems that include one or more servers, such as network servers orcloud servers. In some instances, the graph-based distance metricsmodule 102 can, in part or in whole, be implemented within or configuredto operate in conjunction with a social networking system (or service),such as the social networking system 630 of FIG. 6. It should beunderstood that there can be many variations or other possibilities.

The graph-based distance metrics module 102 can be configured tocommunicate and/or operate with the at least one data store 110, asshown in the example system 100. The data store 110 can be configured tostore and maintain various types of data. In some implementations, thedata store 110 can store information associated with the socialnetworking system (e.g., the social networking system 630 of FIG. 6).The information associated with the social networking system can includedata about users, user identifiers, social connections, socialinteractions, profile information, demographic information, locations,geo-fenced areas, maps, places, events, pages, groups, posts,communications, content, feeds, account settings, privacy settings, asocial graph, and various other types of data. In some embodiments, thedata store 110 can store information that is utilized by the graph-baseddistance metrics module 102. For example, the data store 110 can storeone or more node graphs, distance scores, illegitimate activityidentification rules and/or models, and the like. It is contemplatedthat there can be many variations or other possibilities.

The graph generation module 104 can be configured to generate a nodegraph comprising a plurality of user account nodes. Each user accountnode of the plurality of user account nodes can be associated with auser account, such as a social networking system user account. Certainuser accounts may be labeled as illegitimate user accounts, and theirassociated user account nodes can be labeled as illegitimate useraccount nodes.

The graph generation module 104 can be configured to generate edgesconnecting user account nodes in a node graph. In various embodiments,an edge connecting two user account nodes indicates a sharedcharacteristic between the two user account nodes and, therefore, theuser accounts associated with the two user account nodes. For example,edges can represent IP addresses, with each edge being associated with aparticular IP address. An edge connecting a first user account node anda second user account node can indicate that the first and second useraccount nodes are associated with the same IP address.

In certain embodiments, the graph generation module 104 can generate abipartite node graph, which includes both user account nodes and edgenodes. In such embodiments, an “edge” connecting two user account nodesis represented as an edge node placed between the two user accountnodes. For example, rather than an edge connecting a first user accountnode to a second user account node, the first user account node can beconnected to a first edge node and the first edge node can also beconnected to the second user account node. The shared first edge nodebetween the first and second user account nodes can indicate a sharedcharacteristic between the first and second user account nodes. Forexample, edge nodes can represent IP addresses, with each edge nodebeing associated with a particular IP address. A shared edge nodebetween a first user account node and a second user account node canindicate that the first and second user account nodes are associatedwith the same IP address. A bipartite node graph may provide someadvantages over a node graph having only a single type of node, e.g.,only user account nodes. For example, consider an example scenario inwhich a node graph connects user account nodes based on IP address. If10,000 users share a particular IP address, edges would have to begenerated between every user of the 10,000 users to every other user ofthe 10,000 users. This would result in 10,000*9,999 edges (directededges) being generated for that single IP address alone. Conversely, ina bipartite node graph, the single IP address would be represented by anedge node, and the 10,000 user account nodes could connect to the edgenode. As such, only 10,000 edges are generated in the bipartite nodegraph scenario instead of 10,000*9,999 edges.

In various embodiments, the graph generation module 104 can beconfigured to generate multiple node graphs. Edges and/or edge nodes ineach node graph can be associated with a particular edge characteristic.For example, the graph generation module 104 can be configured togenerate a first node graph in which edge nodes in the first node graphare associated with a first edge characteristic. In furtherance of thisexample, the graph generation module 104 can be configured to generate asecond node graph in which edge nodes in the second node graph areassociated with a second edge characteristic different from the firstcharacteristic, and so forth. Various edge characteristics can include,for example, IP address, computing device identification information,advertisement landing page ID, payment information (e.g., credit cardnumber or credit card BIN), and the like.

By generating multiple node graphs that are each associated with adifferent edge characteristic, the graph generation module 104 cancreate node graphs that demonstrate different relationships between useraccount nodes. For example, a first node graph can demonstraterelationships between user account nodes based on IP address. In thisscenario, each edge node in the first node graph can be associated witha particular IP address. User account nodes that share an edge node inthe first node graph can be understood to have shared an IP address,i.e., the IP address associated with the edge node.

Further to this example, a second node graph can demonstraterelationships between user account nodes based on computing deviceidentification information. In this scenario, each edge node in thesecond node graph can be associated with a set of computing deviceidentification information that identifies a particular computingdevice. User account nodes that share an edge node in the second nodegraph can be understood to share computing device identificationinformation, indicating that the user accounts associated with theseuser account nodes may have been accessed from a common computingdevice.

Further to this example, a third node graph can demonstraterelationships between user account nodes based on advertisement landingpage ID. In this scenario, each edge node in the third node graph can beassociated with a particular advertising landing page ID identifying aparticular landing page. User account nodes that share an edge node inthe third node graph can be understood to be associated with a sharedlanding page, i.e., the landing page identified by the advertisinglanding page ID associated with the edge node.

Further to this example, a fourth node graph can demonstraterelationships between user account nodes based on credit card BIN. Inthis scenario, each edge node in the fourth node graph can be associatedwith a particular credit card BIN. User account nodes that share an edgenode in the fourth node graph can be understood to be associated withthe same credit card BIN, i.e., the credit card BIN that is associatedwith the edge node. Naturally, in other examples, a different number ofnode graphs representing other desired edge characteristics orcombinations of edge characteristics can be used.

The distance score calculation module 106 can be configured to calculatedistance scores for nodes in a node graph. In various embodiments, thedistance score calculation module 106 can be configured to calculatedistance scores for each user account node in a node graph. The distancescore for a particular user account node can represent a minimumdistance from the user account node to a nearest illegitimate useraccount node in the node graph. In certain embodiments, distance can becounted as a number of nodes that must be traveled to go from one nodeto another. As such, a distance score that represents a minimum distancefrom a user account node to a nearest illegitimate user account node canbe measured as a minimum number of nodes that must be traveled to gofrom the user account node to a nearest illegitimate user account node.In a bipartite node graph, it may be the case that distance scores areeven integers, as the distance from one user account node to anotherwill involve traversing an edge node between each user account node. Invarious embodiments, the distance score calculation module 106 can beconfigured to calculate distance scores based on a modifiedbreadth-first search algorithm. For example, the modified breadth-firstsearch algorithm can radiate out from an illegitimate node (e.g.,illegitimate user account node or illegitimate edge node) a distance of1 for each iteration of the modified breadth-first search algorithm.When another node (e.g., a user account node or an edge node) is firstreached, the distance score calculation module 106 can record a currentiteration distance as the other node's distance from the illegitimatenode. For example, one or more nodes that are reached on a firstiteration can have a distance score of 1, and one or more nodes that arereached on a second iteration can have a distance score of 2, and soforth. In certain embodiments, the distance score calculation module 106can be configured to implement a selected distance score cap value. Thedistance score cap value can represent a maximum allowable value for adistance score, such that distance scores that exceed the distance scorecap value are assigned a distance score equal to the distance score capvalue. The distance score cap value may be, for example, 14, which in abipartite node graph represents seven degrees of separation from oneuser account node to another, or any other configurable value. Forexample, if a first user account node is 4 nodes away from a nearestillegitimate user account node, the corresponding distance score of 4falls below the distance score cap value of 14. Therefore, the firstuser account node's distance score would be 4. However, if a second useraccount node is 20 nodes away from a nearest illegitimate user accountnode, the corresponding distance score of 20 would exceed the distancescore cap value. As such, the distance score of the second user accountnode would be set to the distance score cap value of 14. In variousembodiments, the distance score cap value can be implemented byterminating the modified breadth-first search algorithm after a maximumnumber of iterations. For example, as described above, after a firstiteration of the modified breadth-first search algorithm, nodes that areone node away from an illegitimate node can be assigned a distance scoreof 1. After a second iteration, nodes that are two nodes away from anillegitimate node can be assigned a distance score of 2, and so forth.If the maximum number of iterations is set to 14 (i.e., the distancescore cap value is 14), the modified breadth-first search algorithm canbe configured to go through 14 iterations, and then stop. Once themodified breadth-first search algorithm has performed 14 iterations, anynodes that still do not have an assigned distance score can be assigneda distance score equal to the distance score cap value of 14.

In certain embodiments, a distance score for a particular user accountnode may represent a minimum distance from the user account node to anillegitimate edge node. For example, if edge nodes are associated withIP address, a particular IP address may be labeled as a “bad” orillegitimate IP address, and the associated edge node may be labeledsimilarly. Or if edge nodes are associated with credit card BINs, aparticular credit card BIN may be labeled as an illegitimate credit cardBIN, and the associated edge node can be labeled accordingly. Thedistance score for a user account node can represent a minimum distancefrom the user account node to a nearest illegitimate edge node. In abipartite node graph, a distance score from a user account node to anedge node may be an odd integer.

As discussed above, each user account node can be associated with a useraccount. As such, a distance score for a user account node can beassociated with the user account associated with the user account node.By calculating distance scores in the manner described above, a useraccount with a relatively high distance score can indicate a moretrustworthy user account. This is because the high distance scoreindicates that that user account is not closely related to anillegitimate user account, at least with regard to the particular edgecharacteristic depicted in a node graph. Conversely, a relatively lowdistance score can indicate a lack of trustworthiness, since the lowdistance score indicates a close relationship to an illegitimate useraccount or illegitimate edge node.

The distance score calculation module 106 can be configured to calculatea plurality of distance scores for each user account node of a pluralityof user account nodes based on a plurality of node graphs. As discussedabove, a plurality of node graphs can be generated, with each node graphbeing associated with a particular edge characteristic. In certainembodiments, each node graph of the plurality of node graphs can includethe same set of user account nodes. However, each node graph can includea different set of edge nodes, and a different set of connectionsbetween the user account nodes and the edge nodes. As such, distancescores calculated for each user account node will differ from one nodegraph to another. For example, consider a first user account node thatis included in three separate node graphs: a first node graph generatedbased on IP address; a second node graph generated based on credit cardBIN; and a third node graph generated based on advertisement landingpage ID. The first user account node's distance score based on the firstnode graph may be 4, the first user account node's distance score basedon the second node graph may be 12, and the first user account node'sdistance score based on the third node graph may be 10. Distance scorescalculated for a plurality of user accounts can be stored (e.g., in datastore(s) 110) as distance score information.

The illegitimate activity identification module 108 can be configured toidentify illegitimate activity based on distance score information.Distance score information can comprise a plurality of user accounts andone or more distance scores associated with each user account of theplurality of user accounts. The illegitimate activity identificationmodule 108 can be configured to implement one or more illegitimateactivity identification rules for identifying illegitimate activity. Forexample, an illegitimate activity identification rule may specify thattransactions and/or actions by user accounts having an average distancescore below a distance score threshold are to be labeled as illegitimateactivities. In some embodiments, the illegitimate activityidentification module 108 is not part of the graph-based distancemetrics module 102, but rather is part of an overall system to identifyand prevent illegitimate activity on a system, such as a socialnetworking system.

In certain embodiments, the illegitimate activity identification module108 can be configured to determine a probability that a transaction oractivity is illegitimate based on distance score information. Theillegitimate activity identification module 108 can be configured todetermine a probability of illegitimate activity using one or moremachine learning models. For example, the illegitimate activityidentification module 108 can receive a notification of a transactioninvolving a first user account. The illegitimate activity identificationmodule 108 can retrieve distance score information associated with thefirst user account (e.g., from a data store). The illegitimate activityidentification module 108 can determine a probability that a transactionrequested by the first user account is an illegitimate transaction basedon the distance score information and one or more machine learningmodels. If the probability exceeds a probability threshold, thetransaction can be denied or the first user account can be disabled. Ifthe probability does not exceed the probability threshold, thetransaction can be approved.

Various examples of illegitimate activity detection based on one or morerules or models are disclosed in U.S. patent application Ser. No.14/206,180, filed Mar. 12, 2014, issued as U.S. Pat. No. 9,380,065 onJun. 28, 2016, entitled “SYSTEMS AND METHODS FOR IDENTIFYINGILLEGITIMATE ACTIVITIES BASED ON HISTORICAL DATA”; U.S. patentapplication Ser. No. 14/314,924, filed Jun. 25, 2014, entitled “SYSTEMSAND METHODS FOR RANKING RULES THAT IDENTIFY POTENTIALLY ILLEGITIMATEACTIVITIES”; U.S. patent application Ser. No. 15/158,458, filed May 18,2016, entitled “SYSTEMS AND METHODS FOR IDENTIFYING ILLEGITIMATEACTIVITIES BASED ON HISTORICAL DATA”; and U.S. patent application Ser.No. 15/418,536, filed Jan. 27, 2017, entitled “SYSTEMS AND METHODS FORINCORPORATING LONG-TERM PATTERNS IN ONLINE FRAUD PROTECTION,” each ofwhich is incorporated by reference as if fully set forth herein.

FIG. 2 illustrates an example functional block diagram 200 associatedwith illegitimate activity identification based on distance scoreinformation, in accordance with an embodiment of the present disclosure.In the example block diagram 200, a first bipartite node graph 206 isgenerated using a set of user account nodes 202 and a first set of edgenodes 204. The set of user account nodes 202 can represent user accountson a social networking system. The set of user account nodes 202 caninclude one or more illegitimate user account nodes. The first set ofedge nodes 204 can be associated with a first edge characteristic. Forexample, the first set of edge nodes 204 can be associated with IPaddresses, such that each edge node of the first set of edge nodes 204is associated with a particular IP address. The first bipartite nodegraph 206 can connect the user account nodes 202 based on shared IPaddresses. At block 208, distance scores can be calculated for each useraccount node in the set of user account nodes 202 based on the firstbipartite node graph 206. The distance scores can be stored in a datastore 230.

A second bipartite node graph 216 is generated using the set of useraccount nodes 202 and a second set of edge nodes 214. The second set ofedge nodes 214 can be associated with a second edge characteristic. Forexample, the second set of edge nodes 214 can be associated with creditcard BINs, such that each edge node in the second set of edge nodes 214is associated with a particular credit card BIN. At block 218, distancescores can be calculated for each user account node in the set of useraccount nodes 202 based on the second bipartite node graph 216. Thedistance scores can be stored in the data store 230.

A third bipartite node graph 226 is generated using the set of useraccount nodes 202 and a third set of edge nodes 224. The third set ofedge nodes 224 can be associated with a third edge characteristic. Forexample, the third set of edge nodes can be associated with landing pageIDs, such that each edge node in the third set of edge nodes 224 can beassociated with a particular landing page ID identifying a particularlanding page. At block 228, distance scores can be calculated for eachuser account node in the set of user account nodes 202 based on thethird bipartite node graph 226. The distance scores can be stored in thedata store 230. At block 232, distance score information stored in thedata store 230 can be utilized to inform an illegitimate activitydetermination.

FIGS. 3A and 3B illustrate example scenarios 300, 350 associated withgeneration of multiple bipartite node graphs based on various edgecharacteristics, in accordance with an embodiment of the presentdisclosure. FIG. 3A depicts the example scenario 300, including a firstbipartite node graph 302. The first bipartite node graph 302 depictsrelationships for four user accounts as an example. User accounts arerepresented by user account nodes labeled UA1, UA2, UA3, and UA4. Thefirst bipartite node graph 302 depicts relationships for the four useraccounts UA1, UA2, UA3, and UA4 based on IP address. A first edge node304 is associated with a first IP address (IP 1), a second edge node 306is associated with a second IP address (IP 2), a third edge node 308 isassociated with a third IP address (IP 3), and a fourth edge node 310 isassociated with a fourth IP address (IP 4). The first bipartite nodegraph 302 indicates the following relationships between the four useraccounts:

-   -   user accounts UA1 and UA2 have shared IP address IP 1;    -   user accounts UA2 and UA3 have shared IP address IP 2;    -   user accounts UA2 and UA4 have shared IP address IP 3; and    -   user accounts UA3 and UA4 have shared IP address IP 4.

User account UA3 has been labeled as an illegitimate user account.Distance scores can be calculated for each user account using the firstbipartite node graph 302. For example, a distance score may be equal toa distance from a particular user account node to a nearest illegitimateuser account node. Using this definition of distance score, a first setof distance scores can be calculated. The distance score for useraccount UA1 is 4 (i.e., the minimum distance from user account UA1 toillegitimate user account UA3 is 4); the distance score for user accountUA2 is 2, the distance score for user account UA3 is 0, and the distancescore for user account UA4 is 2.

FIG. 3B depicts the example scenario 350, including a second bipartitenode graph 352. The second bipartite node graph 352 depictsrelationships between the four user accounts UA1, UA2, UA3, and UA4based on credit card BIN. A first edge node 354 is associated with afirst credit card BIN (BIN1), a second edge node 356 is associated witha second credit card BIN (BIN2), a third edge node 358 is associatedwith a third credit card BIN (BIN3), and a fourth edge node 360 isassociated with a fourth credit card BIN (BIN4). The second bipartitenode graph 352 indicates the following relationships between the fouruser accounts:

-   -   user accounts UA1, UA2, and UA4 are all associated with credit        card BIN BIN1;    -   user accounts UA1 and UA3 are both associated with credit card        BIN BIN2;    -   user accounts UA1 and UA2 are both associated with credit card        BIN BIN3; and    -   user accounts UA2 and UA3 are both associated with credit card        BIN BIN4.

A second set of distance scores can be calculated based on the secondbipartite node graph 352. In this scenario, the distance score for useraccount UA1 is 2, the distance score for user account UA2 is 2, thedistance score for user account UA3 is 0, and the distance score foruser account UA4 is 4. In this example, the first set of distance scoresand the second set of distance scores can be analyzed, as set forthherein, to identify illegitimate activity associated with a useraccount.

FIG. 4 illustrates an example method 400 associated with identificationof illegitimate transactions based on graph-based distance metrics,according to an embodiment of the present disclosure. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

At block 402, the example method 400 can generate a node graphcomprising a plurality of user account nodes and a plurality of edgenodes connecting the plurality of user account nodes. At block 404, theexample method 400 can calculate a distance score for each user accountnode of the plurality of user account nodes. At block 406, the examplemethod 400 can determine that a transaction is an illegitimatetransaction based at least in part on the distance scores.

FIG. 5 illustrates an example method 500 associated with identificationof illegitimate activity based on graph-based distance metrics from aplurality of node graphs, according to an embodiment of the presentdisclosure. It should be appreciated that there can be additional,fewer, or alternative steps performed in similar or alternative orders,or in parallel, within the scope of the various embodiments discussedherein unless otherwise stated.

At block 502, the example method 500 can generate a plurality of nodegraphs, each node graph of the plurality of node graphs comprising aplurality of user account nodes and a plurality of edge nodes connectingthe plurality of user account nodes, wherein a subset of the pluralityof user account nodes are illegitimate user account nodes. At block 504,the example method 500 can calculate a plurality of distance scores foreach user account node of the plurality of user account nodes, whereinthe distance score for a user account node represents a minimum distancebetween the user account node and a nearest illegitimate user accountnode, and each distance score for a user account node is calculatedbased on a respective one of the plurality of node graphs. At block 506,the example method 500 can determine that a transaction is anillegitimate transaction based at least in part on the distance scores.At block 508, the example method 500 can deny the transaction based onthe determining that the transaction is an illegitimate transaction.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that canbe utilized in various scenarios, according to an embodiment of thepresent disclosure. The system 600 includes one or more user devices610, one or more external systems 620, a social networking system (orservice) 630, and a network 650. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 630. For purposes of illustration, the embodiment of the system600, shown by FIG. 6, includes a single external system 620 and a singleuser device 610. However, in other embodiments, the system 600 mayinclude more user devices 610 and/or more external systems 620. Incertain embodiments, the social networking system 630 is operated by asocial network provider, whereas the external systems 620 are separatefrom the social networking system 630 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 630 and the external systems 620 operate inconjunction to provide social networking services to users (or members)of the social networking system 630. In this sense, the socialnetworking system 630 provides a platform or backbone, which othersystems, such as external systems 620, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices that canreceive input from a user and transmit and receive data via the network650. In one embodiment, the user device 610 is a conventional computersystem executing, for example, a Microsoft Windows compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 610 can be a device having computerfunctionality, such as a smart-phone, a tablet, a personal digitalassistant (PDA), a mobile telephone, etc. The user device 610 isconfigured to communicate via the network 650. The user device 610 canexecute an application, for example, a browser application that allows auser of the user device 610 to interact with the social networkingsystem 630. In another embodiment, the user device 610 interacts withthe social networking system 630 through an application programminginterface (API) provided by the native operating system of the userdevice 610, such as iOS and ANDROID. The user device 610 is configuredto communicate with the external system 620 and the social networkingsystem 630 via the network 650, which may comprise any combination oflocal area and/or wide area networks, using wired and/or wirelesscommunication systems.

In one embodiment, the network 650 uses standard communicationstechnologies and protocols. Thus, the network 650 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network650 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 650 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 610 may display content from theexternal system 620 and/or from the social networking system 630 byprocessing a markup language document 614 received from the externalsystem 620 and from the social networking system 630 using a browserapplication 612. The markup language document 614 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 614, the browser application 612 displays the identifiedcontent using the format or presentation described by the markuplanguage document 614. For example, the markup language document 614includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 620 and the social networking system 630. In variousembodiments, the markup language document 614 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 614 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 620 andthe user device 610. The browser application 612 on the user device 610may use a JavaScript compiler to decode the markup language document614.

The markup language document 614 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies616 including data indicating whether a user of the user device 610 islogged into the social networking system 630, which may enablemodification of the data communicated from the social networking system630 to the user device 610.

The external system 620 includes one or more web servers that includeone or more web pages 622 a, 622 b, which are communicated to the userdevice 610 using the network 650. The external system 620 is separatefrom the social networking system 630. For example, the external system620 is associated with a first domain, while the social networkingsystem 630 is associated with a separate social networking domain. Webpages 622 a, 622 b, included in the external system 620, comprise markuplanguage documents 614 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 630 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 630 may be administered, managed, or controlled by anoperator. The operator of the social networking system 630 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 630. Any type of operator may beused.

Users may join the social networking system 630 and then add connectionsto any number of other users of the social networking system 630 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 630 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 630. For example, in an embodiment, if users in thesocial networking system 630 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user account nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 630 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 630 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 630 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 630 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system630 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 630 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system630 provides users with the ability to take actions on various types ofitems supported by the social networking system 630. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 630 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 630, transactions that allow users to buy or sellitems via services provided by or through the social networking system630, and interactions with advertisements that a user may perform on oroff the social networking system 630. These are just a few examples ofthe items upon which a user may act on the social networking system 630,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 630 or inthe external system 620, separate from the social networking system 630,or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety ofentities. For example, the social networking system 630 enables users tointeract with each other as well as external systems 620 or otherentities through an API, a web service, or other communication channels.The social networking system 630 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 630. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 630 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 630 also includes user-generated content,which enhances a user's interactions with the social networking system630. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 630. For example, a usercommunicates posts to the social networking system 630 from a userdevice 610. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 630 by a third party. Content“items” are represented as objects in the social networking system 630.In this way, users of the social networking system 630 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 630.

The social networking system 630 includes a web server 632, an APIrequest server 634, a user profile store 636, a connection store 638, anaction logger 640, an activity log 642, and an authorization server 644.In an embodiment of the invention, the social networking system 630 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 636 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 630. This information is storedin the user profile store 636 such that each user is uniquelyidentified. The social networking system 630 also stores data describingone or more connections between different users in the connection store638. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 630 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 630, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 638.

The social networking system 630 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 636and the connection store 638 store instances of the corresponding typeof objects maintained by the social networking system 630. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store636 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 630initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 630, the social networking system 630 generatesa new instance of a user profile in the user profile store 636, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 638 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 620 or connections to other entities. The connection store 638may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 636 and the connection store 638 may beimplemented as a federated database.

Data stored in the connection store 638, the user profile store 636, andthe activity log 642 enables the social networking system 630 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 630, user accounts of thefirst user and the second user from the user profile store 636 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 638 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 630. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 630 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 630). The image may itself be represented as a node in the socialnetworking system 630. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 636, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 642. By generating and maintaining thesocial graph, the social networking system 630 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 632 links the social networking system 630 to one or moreuser devices 610 and/or one or more external systems 620 via the network650. The web server 632 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 632 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system630 and one or more user devices 610. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 634 allows one or more external systems 620 anduser devices 610 to call access information from the social networkingsystem 630 by calling one or more API functions. The API request server634 may also allow external systems 620 to send information to thesocial networking system 630 by calling APIs. The external system 620,in one embodiment, sends an API request to the social networking system630 via the network 650, and the API request server 634 receives the APIrequest. The API request server 634 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 634 communicates to the external system 620via the network 650. For example, responsive to an API request, the APIrequest server 634 collects data associated with a user, such as theuser's connections that have logged into the external system 620, andcommunicates the collected data to the external system 620. In anotherembodiment, the user device 610 communicates with the social networkingsystem 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from theweb server 632 about user actions on and/or off the social networkingsystem 630. The action logger 640 populates the activity log 642 withinformation about user actions, enabling the social networking system630 to discover various actions taken by its users within the socialnetworking system 630 and outside of the social networking system 630.Any action that a particular user takes with respect to another node onthe social networking system 630 may be associated with each user'saccount, through information maintained in the activity log 642 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 630 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 630, the action isrecorded in the activity log 642. In one embodiment, the socialnetworking system 630 maintains the activity log 642 as a database ofentries. When an action is taken within the social networking system630, an entry for the action is added to the activity log 642. Theactivity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 630,such as an external system 620 that is separate from the socialnetworking system 630. For example, the action logger 640 may receivedata describing a user's interaction with an external system 620 fromthe web server 632. In this example, the external system 620 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system620 include a user expressing an interest in an external system 620 oranother entity, a user posting a comment to the social networking system630 that discusses an external system 620 or a web page 622 a within theexternal system 620, a user posting to the social networking system 630a Uniform Resource Locator (URL) or other identifier associated with anexternal system 620, a user attending an event associated with anexternal system 620, or any other action by a user that is related to anexternal system 620. Thus, the activity log 642 may include actionsdescribing interactions between a user of the social networking system630 and an external system 620 that is separate from the socialnetworking system 630.

The authorization server 644 enforces one or more privacy settings ofthe users of the social networking system 630. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 620, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems620. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 620 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 620 toaccess the user's work information, but specify a list of externalsystems 620 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 620 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 644 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 620, and/or other applications and entities. Theexternal system 620 may need authorization from the authorization server644 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 644 determines if another user, the external system620, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 630 can include agraph-based distance metrics module 646. The graph-based distancemetrics module 646 can, for example, be implemented as the graph-baseddistance metrics module 102, as discussed in more detail herein. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities. For example, in some embodiments, oneor more functionalities of the graph-based distance metrics module 646can be implemented in the user device 610.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the embodiments described herein according to an embodiment ofthe invention. The computer system 700 includes sets of instructions forcausing the computer system 700 to perform the processes and featuresdiscussed herein. The computer system 700 may be connected (e.g.,networked) to other machines. In a networked deployment, the computersystem 700 may operate in the capacity of a server machine or a clientmachine in a client-server network environment, or as a peer machine ina peer-to-peer (or distributed) network environment. In an embodiment ofthe invention, the computer system 700 may be the social networkingsystem 630, the user device 610, and the external system 620, or acomponent thereof. In an embodiment of the invention, the computersystem 700 may be one server among many that constitutes all or part ofthe social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 700 includes a high performanceinput/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710couples processor 702 to high performance I/O bus 706, whereas I/O busbridge 712 couples the two buses 706 and 708 to each other. A systemmemory 714 and one or more network interfaces 716 couple to highperformance I/O bus 706. The computer system 700 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 718 and I/O ports 720 couple to the standard I/Obus 708. The computer system 700 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 708. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Moreover, the computer system 700 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 that, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory 714and then accessed and executed by the processor 702. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a computing system, a graph comprising a plurality ofuser account nodes, a plurality of edge nodes, and a plurality ofconnections connecting the plurality of user account nodes to theplurality of edge nodes, wherein a first edge node of the plurality ofedge nodes represents a shared characteristic between at least two useraccount nodes of the plurality of user account nodes and each node ofthe at least two user account nodes is connected to the first edge noderepresenting the shared characteristic; calculating, by the computingsystem, a first distance score for a first user account node of theplurality of user account nodes, wherein the first distance scorerepresents a minimum distance from the first user account node to anearest illegitimate node; and determining, by the computing system,that a transaction associated with the first user account node is anillegitimate transaction based on the first distance score.
 2. Thecomputer-implemented method of claim 1, further comprising: calculating,by the computing system, a second distance score for a second useraccount node of the plurality of user account nodes, wherein the seconddistance score represents a minimum distance from the second useraccount node to the nearest illegitimate node, wherein the seconddistance score is greater than the first distance score; anddetermining, by the computing system, that the first user account nodeis less trustworthy than the second user account node.
 3. Thecomputer-implemented method of claim 1, wherein the first distance scoreis calculated based on an iterative algorithm that terminates after amaximum number of iterations and assigns a distance score cap value tothe first user account node after the maximum number of iterations. 4.The computer-implemented method of claim 1, further comprising:generating, by the computing system, a plurality of node graphs, eachnode graph of the plurality of node graphs comprising the plurality ofuser account nodes, the plurality of edge nodes, and the plurality ofconnections connecting the plurality of user account nodes to theplurality of edge nodes, wherein edge nodes of each node graph of theplurality of node graphs are associated with a particular edgecharacteristic between at least two user account nodes of the nodegraph, and wherein the calculating a first distance score for the firstuser account node of the plurality of user account nodes comprisescalculating a plurality of distance scores for the first user accountnode of the plurality of user account nodes, each distance score of theplurality of distance scores being calculated based on a respective oneof the plurality of node graphs, wherein the first distance score forthe first user account node is calculated based on the plurality ofdistance scores.
 5. The computer-implemented method of claim 4, furthercomprising: determining, by the computing system, a probability ofillegitimate activity using one or more machine learning models based onthe plurality of distance scores.
 6. The computer-implemented method ofclaim 1, wherein the nearest illegitimate node is a user account node ofthe plurality of user account nodes.
 7. The computer-implemented methodof claim 6, wherein the first user account node is labeled as anillegitimate node based on the first distance score.
 8. Thecomputer-implemented method of claim 1, wherein the nearest illegitimatenode is an edge node of the plurality of edge nodes.
 9. Thecomputer-implemented method of claim 8, wherein the edge node is labeledas an illegitimate node.
 10. The computer-implemented method of claim 1,wherein the shared characteristic comprises at least one of: IP address,advertisement landing page ID, computing device identificationinformation, or payment information.
 11. A system comprising: at leastone processor; and a memory storing instructions that, when executed bythe at least one processor, cause the system to perform a methodcomprising: generating a graph comprising a plurality of user accountnodes, a plurality of edge nodes, and a plurality of connectionsconnecting the plurality of user account nodes to the plurality of edgenodes, wherein a first edge node of the plurality of edge nodesrepresents a shared characteristic between at least two user accountnodes of the plurality of user account nodes and each node of the atleast two user account nodes is connected to the first edge noderepresenting the shared characteristic; calculating a first distancescore for a first user account node of the plurality of user accountnodes, wherein the first distance score represents a minimum distancefrom the first user account node to a nearest illegitimate node; anddetermining that a transaction associated with the first user accountnode is an illegitimate transaction based on the first distance score.12. The system of claim 11, wherein the instructions further cause thesystem to perform: calculating a second distance score for a second useraccount node of the plurality of user account nodes, wherein the seconddistance score represents a minimum distance from the second useraccount node to the nearest illegitimate node, wherein the seconddistance score is greater than the first distance score; and determiningthat the first user account node is less trustworthy than the seconduser account node.
 13. The system of claim 11, wherein the firstdistance score is calculated based on an iterative algorithm thatterminates after a maximum number of iterations and assigns a distancescore cap value to the first user account node after the maximum numberof iterations.
 14. The system of claim 11, wherein the instructionsfurther cause the system to perform: generating a plurality of nodegraphs, each node graph of the plurality of node graphs comprising theplurality of user account nodes, the plurality of edge nodes, and theplurality of connections connecting the plurality of user account nodesto the plurality of edge nodes, wherein edge nodes of each node graph ofthe plurality of node graphs are associated with a particular edgecharacteristic between at least two user account nodes of the nodegraph, and wherein the calculating a first distance score for the firstuser account node of the plurality of user account nodes comprisescalculating a plurality of distance scores for the first user accountnode of the plurality of user account nodes, each distance score of theplurality of distance scores being calculated based on a respective oneof the plurality of node graphs, wherein the first distance score forthe first user account node is calculated based on the plurality ofdistance scores.
 15. The system of claim 14, wherein the instructionsfurther cause the system to perform: determining a probability ofillegitimate activity using one or more machine learning models based onthe plurality of distance scores.
 16. A non-transitory computer-readablestorage medium including instructions that, when executed by at leastone processor of a computing system, cause the computing system toperform a method comprising: generating a graph comprising a pluralityof user account nodes, a plurality of edge nodes, and a plurality ofconnections connecting the plurality of user account nodes to theplurality of edge nodes, wherein a first edge node of the plurality ofedge nodes represents a shared characteristic between at least two useraccount nodes of the plurality of user account nodes and each node ofthe at least two user account nodes is connected to the first edge noderepresenting the shared characteristic; calculating a first distancescore for a first user account node of the plurality of user accountnodes, wherein the first distance score represents a minimum distancefrom the first user account node to a nearest illegitimate node; anddetermining that a transaction associated with the first user accountnode is an illegitimate transaction based on the first distance score.17. The non-transitory computer-readable storage medium of claim 16,wherein the instructions further cause the computing system to perform:calculating a second distance score for a second user account node ofthe plurality of user account nodes, wherein the second distance scorerepresents a minimum distance from the second user account node to thenearest illegitimate node, wherein the second distance score is greaterthan the first distance score; and determining that the first useraccount node is less trustworthy than the second user account node. 18.The non-transitory computer-readable storage medium of claim 16, whereinthe first distance score is calculated based on an iterative algorithmthat terminates after a maximum number of iterations and assigns adistance score cap value to the first user account node after themaximum number of iterations.
 19. The non-transitory computer-readablestorage medium of claim 16, wherein the instructions further cause thecomputing system to perform: generating a plurality of node graphs, eachnode graph of the plurality of node graphs comprising the plurality ofuser account nodes, the plurality of edge nodes, and the plurality ofconnections connecting the plurality of user account nodes to theplurality of edge nodes, wherein edge nodes of each node graph of theplurality of node graphs are associated with a particular edgecharacteristic between at least two user account nodes of the nodegraph, and wherein the calculating a first distance score for the firstuser account node of the plurality of user account nodes comprisescalculating a plurality of distance scores for the first user accountnode of the plurality of user account nodes, each distance score of theplurality of distance scores being calculated based on a respective oneof the plurality of node graphs, wherein the first distance score forthe first user account node is calculated based on the plurality ofdistance scores.
 20. The non-transitory computer-readable storage mediumof claim 19, wherein the instructions further cause the computing systemto perform: determining a probability of illegitimate activity using oneor more machine learning models based on the plurality of distancescores.